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import sys
import os
import logging
import re
import functools
import fnmatch
import numpy as np
def setup_logger(distributed_rank=0, filename="log.txt"):
logger = logging.getLogger("Logger")
logger.setLevel(logging.DEBUG)
# don't log results for the non-master process
if distributed_rank > 0:
return logger
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.DEBUG)
fmt = "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s"
ch.setFormatter(logging.Formatter(fmt))
logger.addHandler(ch)
return logger
def find_recursive(root_dir, ext='.jpg'):
files = []
for root, dirnames, filenames in os.walk(root_dir):
for filename in fnmatch.filter(filenames, '*' + ext):
files.append(os.path.join(root, filename))
return files
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.initialized = False
self.val = None
self.avg = None
self.sum = None
self.count = None
def initialize(self, val, weight):
self.val = val
self.avg = val
self.sum = val * weight
self.count = weight
self.initialized = True
def update(self, val, weight=1):
if not self.initialized:
self.initialize(val, weight)
else:
self.add(val, weight)
def add(self, val, weight):
self.val = val
self.sum += val * weight
self.count += weight
self.avg = self.sum / self.count
def value(self):
return self.val
def average(self):
return self.avg
def unique(ar, return_index=False, return_inverse=False, return_counts=False):
ar = np.asanyarray(ar).flatten()
optional_indices = return_index or return_inverse
optional_returns = optional_indices or return_counts
if ar.size == 0:
if not optional_returns:
ret = ar
else:
ret = (ar,)
if return_index:
ret += (np.empty(0, np.bool),)
if return_inverse:
ret += (np.empty(0, np.bool),)
if return_counts:
ret += (np.empty(0, np.intp),)
return ret
if optional_indices:
perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
aux = ar[perm]
else:
ar.sort()
aux = ar
flag = np.concatenate(([True], aux[1:] != aux[:-1]))
if not optional_returns:
ret = aux[flag]
else:
ret = (aux[flag],)
if return_index:
ret += (perm[flag],)
if return_inverse:
iflag = np.cumsum(flag) - 1
inv_idx = np.empty(ar.shape, dtype=np.intp)
inv_idx[perm] = iflag
ret += (inv_idx,)
if return_counts:
idx = np.concatenate(np.nonzero(flag) + ([ar.size],))
ret += (np.diff(idx),)
return ret
def colorEncode(labelmap, colors, mode='RGB'):
labelmap = labelmap.astype('int')
labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3),
dtype=np.uint8)
for label in unique(labelmap):
if label < 0:
continue
labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \
np.tile(colors[label],
(labelmap.shape[0], labelmap.shape[1], 1))
if mode == 'BGR':
return labelmap_rgb[:, :, ::-1]
else:
return labelmap_rgb
def accuracy(preds, label):
valid = (label >= 0)
acc_sum = (valid * (preds == label)).sum()
valid_sum = valid.sum()
acc = float(acc_sum) / (valid_sum + 1e-10)
return acc, valid_sum
def intersectionAndUnion(imPred, imLab, numClass):
imPred = np.asarray(imPred).copy()
imLab = np.asarray(imLab).copy()
imPred += 1
imLab += 1
# Remove classes from unlabeled pixels in gt image.
# We should not penalize detections in unlabeled portions of the image.
imPred = imPred * (imLab > 0)
# Compute area intersection:
intersection = imPred * (imPred == imLab)
(area_intersection, _) = np.histogram(
intersection, bins=numClass, range=(1, numClass))
# Compute area union:
(area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
(area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
area_union = area_pred + area_lab - area_intersection
return (area_intersection, area_union)
class NotSupportedCliException(Exception):
pass
def process_range(xpu, inp):
start, end = map(int, inp)
if start > end:
end, start = start, end
return map(lambda x: '{}{}'.format(xpu, x), range(start, end+1))
REGEX = [
(re.compile(r'^gpu(\d+)$'), lambda x: ['gpu%s' % x[0]]),
(re.compile(r'^(\d+)$'), lambda x: ['gpu%s' % x[0]]),
(re.compile(r'^gpu(\d+)-(?:gpu)?(\d+)$'),
functools.partial(process_range, 'gpu')),
(re.compile(r'^(\d+)-(\d+)$'),
functools.partial(process_range, 'gpu')),
]
def parse_devices(input_devices):
"""Parse user's devices input str to standard format.
e.g. [gpu0, gpu1, ...]
"""
ret = []
for d in input_devices.split(','):
for regex, func in REGEX:
m = regex.match(d.lower().strip())
if m:
tmp = func(m.groups())
# prevent duplicate
for x in tmp:
if x not in ret:
ret.append(x)
break
else:
raise NotSupportedCliException(
'Can not recognize device: "{}"'.format(d))
return ret
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